PERFORMANCE PREDICTION OF PARALLEL SELF CONSISTENT FIELD COMPUTATION
作者:
J. PAPAY,
T. J. ATHERTON,
M. J. ZEMERLY,
G. R. NUDD,
期刊:
Parallel Algorithms and Applications
(Taylor Available online 1996)
卷期:
Volume 10,
issue 1-2
页码: 127-143
ISSN:1063-7192
年代: 1996
DOI:10.1080/10637199608915612
出版商: Taylor & Francis Group
关键词: Performance prediction;processor activity graph;self consistent field computations
数据来源: Taylor
摘要:
This paper presents a methodology for performance prediction of parallel algorithms and illustrates its use on a large scale computational chemistry application. The performance prediction uses a component time characterization technique which splits up the sequential code into computational components and measures the time for each of them. The parallel algorithm is built from these components by adding communication routines. A “Processor Activity Graph” (PAG) providing a graphical representation of the parallel algorithm runtime behaviour is used for predicting the execution time. For a case study a Self Consistent Field (SCF) computation has been selected which forms the basis of many computational chemistry packages [4, 5]. The performance model of SCF computation has been built and the prediction have been compared with the results of measurements. The measurements have been provided on a mesh connected distributed memory parallel computer (128 T800 Parsytec SuperCluster). The prediction error is less than 10%. Performance optimisation of the application has been achieved by reducing the communication overhead and changing the data representation.
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